Development of a Wearable Torsion Sensor Utilizing a Geometric Model and a Hysteresis-Compensated Calibration Engine

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Abstract
Measuring torsional movement is crucial for tracking human kinematics in applications such as virtual reality, biomechanics, rehabilitation, and soft robotic joints. However, continuous, non-invasive torsion measurement remains challenging due to multi-axial deformations, where existing methods rely heavily on empirical data fitting or computationally expensive deep learning models requiring vast training datasets. To address these limitations, this paper proposes a training-free, highly precise torsion estimation framework that converts soft resistive strain sensor data into forearm rotation angles using closed-form kinematic-geometric modeling. Two geometric representations—the Cylinder Model and Truncated Cone Model—are established to analytically map non-linear tensile strain to torsion angles. Additionally, an online calibration algorithm is integrated to mitigate cumulative measurement drift caused by viscoelastic hysteresis. Under static conditions, the proposed models achieved a high coefficient of determination (R2 = 0.9888) against reference IMU data. In dynamic trials, the hysteresis compensation algorithm maintained robust tracking performance (R2 = 0.8033), achieving an angular root-mean-square error (RMSE) of 4.12° and a cycle peak prominence relative error of 0.42% for the Truncated Cone Model. In this single-subject proof-of-concept feasibility study (n = 1), the proposed framework demonstrated the feasibility of continuous torsional motion tracking through a training-free geometric approach, providing a preliminary foundation for future evaluation in wearable systems and soft robotics.
Funding Information
  • Ministry of Trade, Industry and Energy (P0022335)

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